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Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey

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arxiv 2410.19872 v1 pith:IY5GNP4U submitted 2024-10-24 cs.CV

classification cs.CV
keywords fusiondetectionobjectradar-cameratrackingcameraperceptionradar
verification ladder T0 review T1 audit T2 compute T3 formal
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Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera fusion has prompted prospective research directions owing to its extensive applicability, complementarity, and compatibility. Nonetheless, there still lacks a systematic survey specifically focusing on deep fusion of radar and camera for object detection and tracking. To fill this void, we embark on an endeavor to comprehensively review radar-camera fusion in a holistic way. First, we elaborate on the fundamental principles, methodologies, and applications of radar-camera fusion perception. Next, we delve into the key techniques concerning sensor calibration, modal representation, data alignment, and fusion operation. Furthermore, we provide a detailed taxonomy covering the research topics related to object detection and tracking in the context of radar and camera technologies.Finally, we discuss the emerging perspectives in the field of radar-camera fusion perception and highlight the potential areas for future research.

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  1. CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Forecasting future LiDAR from historical camera–radar inputs pretrains a transferable CR BEV backbone that improves long-horizon geometry prediction and many nuScenes driving tasks.

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